The Co-Intelligence Revolution: How Humans and AI Co-Create New Value

28:42We give the example of like Can't Me Go, for instance, where they've designed it for active learning. It doesn't give you the answer. It kind of works with you until you get the answer and the teacher might want to design it. But, you know, in terms of how long should that go before maybe you reveal the answer. Sometimes we also learn from the answers and so on. So there's a lot of science and art that goes into designing these systems in ways in which, you know, the student feels empowered to learn and is actually engaged and is learning. But that's a very different process because in the old model as a teacher, it's very difficult for me to personalize the way I want to describe something so you understand it.

29:19Because I don't know what will click with you. But that's what the AI does very well, right? You can ask it to explain something like give me a sports analogy or help me understand it in this context. So its ability to actually change the way something is described is phenomenal. Now, if you're a teacher, how do you look at this? It's a risk. It seems like I'm going to be replaced, right? But if you actually see the world that you're moving into, we want to be able to train students to actually be able to work and engage with the AI systems because in the workplace, presumably now the task is to manage, design, you know, and enhance these systems even as it augments their own capabilities, right?

30:06But if you keep that as the goal, yes, it comes with challenges and risks, but now it's very interesting what it does is it forces you to think about, hey, how do you therefore evaluate students? And to me, and I've just started experimenting in my class where they all use child GPTs, so what is my role now? Well, actually, I have to design it in a way in which they enhance the learning, but the focus shifts from migrating answers, because CharChipity gives answers to things, to how well do you question? So how well do you craft questions? How well do you use the system to frame problems? Individually working with CharChipity or working in teams, right?

30:45So it shifts the nature of the way you think about assignments, right? And so because they are now interacting. And so you're evaluating how well they interact because in the workplace, presumably that's what they're going to be doing in the future. So in a way you're saying, don't show me what you've done, show me how you've thought about it. Yeah, but then you need to evaluate that, right? And so that is, I think now, that's where things are evolving now in terms of being able to allow them to learn, you know, at their pace in the way in which they want to, allow them to creatively express their agency in the process.

31:18But then our role is to actually build this architecturally to actually facilitate that co-intelligent, you know, co-creation of their learning experience. I mean, and once again, just thinking out aloud in terms of how and presumably, I mean, in Google, you must be working on these ideas. But we briefly touch upon it in the book. But things like a world model, does it understand, like a baby understands from just looking at the world and getting a sense of the world? How does the AI understand the world model in how it responds to a situation? That could be the next form of interaction. I mean, I think even for the AI system to say, I don't know, I think that's the next level of development, right?

32:08I mean, there is no clear objective function right now. I don't know what to do here. And so for the system to come back to the user saying, now tell me what you think and having that dialogue, that's another form of evolution, if you will, for the AI system in forms of potential dialogue that it could have with humans to understand. So maybe just pushing a little more in this direction. We've spoken a bit about how humans, whether they're students or educators or business professionals, interact with AI. But I'm assuming as this goes along, this is going to have a lot of implications for how our organizations are designed, how they interact with each other.

32:52And these organizations are not necessarily only for-profit businesses. This could have implications for governments, for universities, for a whole range of types of organizations. How do you think about the impact of co-intelligence becoming a more natural, normal way of working, way of doing things? How does that impact the design of organizations, how we manage them, how we run them? Yes. So in the book, we introduced the idea of the organization being thought of as a living system. So we call it a co-creative living system organization, right? Because if you go back to kind of where we started, what we are saying is that, you know, if you take our biological systems, we are also very adaptive, right?

33:34We are living systems that we interact and engage with the world. So while the organization may have, let's say, a digital brain, however, the organization kind of processes a lot of this intelligence, right? And engages back with people. The point is that we need to build the management systems in ways in which they function like living systems. So what does that mean? Clearly, the systems have to be very adaptive and very responsive at the individual level, right, in teams. So we have already, you know, things like co-pilot and so on, right, in the flow of work. But that's just in terms of designing workflows, right, and work artifacts and so on.

34:13But if you were to step back and say, and going back to risk management, right, How do we govern these systems? So it puts a lot of emphasis on governance, not just strategy and executing strategy. So how do we build these governance, you know, architectures and management systems? And remember, again, going back to, if you look at product management systems, right? So now you have to worry about experience quality. So what we do in the book is to say, depending on the context, if you're looking at supply chain management, like what changes there? If you're looking at product management, what changes there?

34:42If you're looking at marketing sales service, what changes there? If you're looking at talent management, You know, what changes there? So I think if you look at each set of like management activities, we say, okay, how are the interactions changing there? Because we're moving from just the activity itself to how people engage with AI in that activity. So we need to think through at that kind of granular level, at that micro level, to really say, you know, what changes need to be made? Because that ultimately gets then reflected in the various management systems and processes, right? So it's not like a top-down approach as much as this bottom-up redesign of management systems, but in a very co-creative way.

35:21In fact, the systems itself are getting co-created. And so the platforms that we build, right, in terms of whether it's performance management or various aspects of strategy management, all those will change. And so we take different types of management activities in the book and we feature examples of people who, with respect to that context, are making that shift in terms of redesigning their management system. And if you take all of these things, then we're really talking about what we call a co-intelligent enterprise of the future. But it's not something that we can define up front. It works with co-intelligence all across its offering system, its value chain system, and its management activities.

36:06So that's the way we see it. I just want to add two other points to what Venkat talked about. for me one of the important things in this new way of working if you will is the value creation in the flow of work every moment of interaction becomes important and so the role of the manager as a creative experiencer in maximizing that value creation in every moment of engagement so you need to give that capability to that employee in that thing. So as an organization, do you have that kind of a co-intelligence knowledge environment? So we argue that you need to think about it. You need to create that kind of an environment.

36:57So supply chain manager, you have now discovered that there is a flood in some place. And so now what do you do? At this moment, is there a knowledge environment available for you to maybe do some simulations of alternate availability, what kind of timeframes, what kind of, you know, and so maybe you will now discover that, look, only 80 % of the orders I can fulfill with these two. But that's a business decision that you can now take. You can go back to your, you know, your higher ups and say, this is what I simulated and So I'm saying this ability for providing that capability to the manager, not just as a passive user, but as a creative experiencer.

37:47To participate in that, engage with the system, share certain inputs, and then simulate some scenarios. That is one very important requirement in this new world. Are you creating that kind of environment? If we can actually build on that, what you're saying is we're moving to real-time value creation. right so that's what you know so so if you actually take what he was describing we have now the ability he used the word simulation to simulate like a digital twin of the entire enterprise so one of the things we are seeing and we feature in the book is that digital twins will become very pervasive in this life experts right because it's a representation of the interactions you have in the real world with the system environment but this time it's different because these digital twins are not, digital twins have existed before.

38:32You would go, you would simulate some things, and then you would make some policy changes, strategy changes, you implement that, and you see what happens. But these digital twins are actually linked to the real world because increasingly everything is becoming software-defined, right? So if you take a factory today, the intelligence exists in the physical factory via all of the sensors and controllers and so on. And we give the example of Siemens, for instance. And so the Siemens digital twin is actually connected to that real world. It's not just a mere representation and you do a simulation.

39:02So using tools like NVIDIA, Omnibus, and so on, you can actually simulate changes in this, let's say, you know, a factory that you're building. So you have the AI factory, you actually have a virtual representation of the factory built on top, and then you have the real factory. So there are three layers. But these are connected. So as you make changes, you can see what effects it might have. And going back to your risk point, you can actually manage risks much better. which we feature in the book, lots of examples like that. But the beauty is, once we have decided, this is the decision we're going to take, looked at all the pros and cons, and making informed decisions, you can hit a button, and you actually see the real factory floor change accordingly, because it's software-defined, because it changes the software in the real factory.

39:49That is huge, because one of the benefits we found is that, in designing the factory, things become much more collaborative, because you can invite people because it's less risk. And so they said there's also this thing called, they're building it for engineers, and they call it immersive engineering, where now if I put on goggles and now if I bring in the AR, VR aspect into that my manager life experts in that moment, I can engage with it, and I can speak in my natural language. I can speak German, you can speak French. And so actually for the first time, you can really truly tap into the power of this interactive collaboration where we are not just having like a Google Meet or in a Zoom call.

40:30We're actually looking at the factory floor and someone can move things around and we can see what effects it has and they may have some opinions about it. And we can really have these very rich, immersive conversations. And going back to your point you made earlier about intuition, we can bring our collective intuition, right? But I think the key here is we must have the customer as part of this, which is what they were saying. In that case, the factory floor, the customers, are the people who are the employees and supervisors. So they make sure they are also part of it and not just the manager level people.

41:01So when you extend this to making it more inclusive, suddenly you are able to create value that actually people feel is getting realized for them so that the shop floor person has a better experience, right? The managers who manage those systems have a better experience. So in some sense, we are saying that we can enhance the experiences of everyone involved. that's what the opportunity is before us while we risk manage it effectively, especially with the digital twins. This is interesting. When we were talking earlier, I was describing when I was in college and studying industrial engineering, this kind of simulation that you're talking about to develop an intuition for what happens when you make decisions in a factory was a mathematical simulation tool.

41:47And I remember when... It's like using MATLAB or something. Exactly. And these were built using systems. And there are a few universities that actually built like these little, what they called virtual factories, but basically they were mathematical simulations. They were not really connected to a real world shop floor. I remember in one of my early jobs in industry, I used to work for a railroad as an operations research analyst. We tried simulating the operating plan, try and understand where various risks lie, right? Because a forecast has variance, all of these things. But again, like you said, it wasn't connected to the real world.

42:20So I think in what you're describing, there's a lot of excitement in some ways because I could imagine future generations of could be engineers, could be economists, et cetera, coming out, whose learning, even though it's still in the academic space, has a lot more of a deeper connection to reality. So, yeah. Absolutely. This is absolutely fascinating. I'm also mindful of time, but I also think that given that we've been talking about co-intelligence and you've written books on co-creation, there's a chance to co-create something by throwing a question back at me and Google while I pull up some of the questions that Googlers have prepared.

42:57We were actually planning on doing that. So, Sharath, last time when we came and spoke to you, because we heard a bit about your work, when you are the architect of the GP story in India, right? So, if you were to, having read the book and having had some discussions about the co-intelligence, If you were to sort of apply that to the GP ecosystem in India, FinTech ecosystem in India, what might be some two, three new things that you would like to do? And at this point, there's a lot of stuff racing through my mind. But I think the two things that I'll probably point out that have stood out from this talk for me that apply to this question are, one, this notion of understanding life interactions a lot better.

43:51And the second one is where you're talking about having the customer involved in the co-creation as well, that L 'Oreal example of the person almost being able to produce the specific kind of product that they need. If I try to find those parallels in fintech, what I would say is that we've up until now generally been used to the idea of creating products where we tell people, look, you're looking for a particular function or you're looking for a particular product. And there's a process that you go through to get it. So if you want to make a payment, you go choose a method of payment, you authenticate yourself, you say where that payment is going, all of that stuff.

44:32And there's a particular flow that you go through. That flow looks somewhat similar for all of us, right? Whether it is Google Pay or many other applications. If we took something more complex, like I want to get access to credit, we put them through a longer flow. We say, not only is it important enough to authenticate yourself, but I need details like your name, date of birth, some identifiers that let me put a credit report. If that isn't available, give me alternate information that helps me underwrite and so on. But imagine now that the user comes in talking about what they want to do. And it is not just that we're able to tailor the process.

45:09We could actually even create the right experience for them on the fly. So I could have a user who, from your example, if it's the farmer in, let's say, Maharashtra and prefers an interface that works in Marathi, is someone who's not taken a loan before, is not completely aware of various schemes they might be eligible for from the government. We do a more elaborate, simple interface for them in Marathi. But if it was one of you who is a more tech-savvy individual, who's comfortable with financial products, etc., we might even speed up that process by introducing some of the jargon that brings efficiency.

45:45Because you probably understand some of those things, right? And therefore, there's no point in putting you through what would appear to be a very long experience. I think between these two, the thing that I don't have a good answer for, and which goes back to this question of risk is at the same time to ensure that there's a level playing field, that we're not discriminating against people as they get into access to products, to ensure that because it's a financial product, we're making the right disclosures so that people know what they're getting into. How do I maintain that baseline of making sure there is sufficient transparency in the process, that your privacy is respected, that you have a secure experience, and that it happens, all of this happens in a timely fashion.

46:31And that is a harder one to solve for. And that is where I think, you know, I'd go back to this question of life experiences because you need to have that intuition, put yourself in that user's shoes and say, why are they trying to do this? But if I may, if you actually think of it as a life experience ecosystem, then when you asked the question, we said FinTech ecosystem. But that's just slotting you in terms of FinTech. So one of the things as you're talking, as reminded of my recent lived experience, where since I came in from the US and my mother lives here, she wanted to make my favorite dish for me, which involves some kind of spinach.

47:07And so there's a vegetable vendor whom she gets the spinach from, and it's amazing how she knows what kind of spinach and she brings exactly the right quantity. So she does demand forecasting very well. But she's got a limited physical space in the cart. So I was just, and by the way, so payment is so easy here, I just scan the QR code and use GPM, done. And for her, that's great. I was talking to her. She said she doesn't carry cash around. It's much more secure. And you get all this stuff. But then when I was saying, hey, you know, it's like Aladdin in the magic lab. If you had a wish, what would that be?

47:43It's very interesting. She wanted to have another card. She wants to grow her business, right? I mean, she would use words like growing business. She said, oh, it would be nice if I could do this because there's only so much I can do. I wish I could have another card. But if you actually, so going back to what you're saying, there are two things. One is, how can she express that intent? So if you think of it as just a financial app, okay, it's doing the transaction. No, but what if you had something there, a chat saying, hey, you know, is there somewhere, you know, how would you like to improve your life, right, your livelihood?

48:13And the person says, well, I would like to, you know, have another card. What she's implicitly saying, perhaps for you might be, hey, I might need another loan to actually get a card. but she may not ask for a loan. So that's one thing that struck me. The other one is, as you start then thinking about that, what she's saying is, hey, I need another cart. Now, are you in the business of building carts? Not necessarily, but it means that from your ecosystem perspective, you could create other kinds of services, right, that can kind of plug into it. Like going back to the farmer example now, you know, ITC has built this platform.

48:46It's almost like a marketplace, right, where different people provide fertilizers, pesticides, you know, other things in a way in which the farmer wants to utilize them, right, in order to grow his income. So similarly here, it basically means going outside of the kind of the narrow confines of the fintech ecosystem, right? It might involve other interconnected ecosystems. So I think it also allows, it brings a lot of opportunity, but we need to really broaden our notion of what the offering is. Yeah, this is fascinating. I think lifting it from the question of a user who knows they want to loan to a user who may not even be able to express it in that form, or not necessarily not able to, but maybe hasn't thought yet to the point of whether they want to do it through a loan or something else.

49:28But start from the actual need of saying, you know, I want to grow my business and the way I think about it is if I had one more cart that I could rent out to someone else, I've got a new source of income. It's fascinating to think about. There's a lot of opportunities, small and medium enterprises, if you were to just generalize for the example, which is totally untapped. Yeah, and this also probably ties back to what you were saying about shared digitalized infrastructures because when you get these more general kind of, you know, needs that people express, I may not have all of the building blocks to be able to build a solution for them.

50:02But if there is a way for me to connect other pieces and build that, and I think that's part of the magic of what's happening in India with a lot of the digital public infrastructure. But I think if we can build on top of that, then it becomes a lot easier to build that users. You can plug in different components from other ecosystems. Exactly, exactly. Now, this is fascinating. I do want to also bring up a question that someone from our team had submitted. They were looking at your book and they went, you know, your premise is fascinating. Do you regard co-creation as a way out of the current human versus robot dilemma that surrounds all the AI narratives that we see today?

50:39And what do you envision when you think about co-intelligence in the context of healthcare and life sciences? I'm sorry, I didn't capture the name of the person who submitted the question, but came from other Googlers, so I thought I should think that. I mean, if you could have an interactive interaction with the person, then we could clarify the question. So I don't know if I'm interpreting the question right. But, yeah, in terms of human and machine, right, yes. I mean, broadly speaking, I think, yes, because we are thinking in either-or terms, when you say, you know, of machines replacing us humans.

51:15So what if machines, you know, what if AI is not here to replace us humans, but our premise is what is that? if it's there to co-create with us. But that's up to us, right, in terms of how we view these AI systems. So I think in some sense, there's a lot of the narrative which is looking at AI replacing humans. And yes, it will do several tasks better than as humans. But then the point is, in human history, we have always elevated ourselves in terms of how we build these systems. I mean, like I said, we have built these AI systems and granted now the AI systems has the potential to self-build itself.

51:48But keeping that aside, and as to when and how it might happen. And also, as humans, we have a role to play in ensuring, you know, speaking of risk and guard railing in ways in which, you know, we can steer it in ways in which it actually serves humanity and not the other way around, us serving AI, right? So from that perspective, absolutely, co-creation is a way out, to go back to the question, because it's both in creating these systems and also creating through it, right? how it affects, and being able to understand those effects faster, like we said, using all of the inputs from people's lived experiences of engaging with AI.

52:28So you need all of these pieces, not just that infrastructure and platforms and focusing on the flow of engagements, but also once they've had experiences, how do you bring those experiences back, which goes beyond reinforcement learning, where we are trying to improve the system, but actually letting the system understand that, hey, that we as humans have a lived experience of the world that is subjective, right? Which the AI system doesn't. And I think that's the role we have to play to teach it. What our lived life experience are all about. And so that, going back to what you said, that world model, right?

53:02And representing that and getting it to understand the world is important. The last thing I would say is that when we think, sometimes we conflate two things. The way a robot perceives the world is not how us humans perceive the world, right? So I think sometimes we conflate the two. For instance, you know, when I came into, for this talk at Google, if you were to ask me, hey, you know, what is the color of the wall you just passed by, et cetera, I wouldn't know. But if I was a robot walking in, you can go and you can look further. It'll tell you the color. It may even tell you where that material came from, et cetera.

53:35But that's not part of our human intelligence, right? So in some sense, as we operate in the world, and there's the system one and system two of Kahneman, one is automatic and this. And we're still trying to understand the human brain, how we cognize the world and engage with it. So I think all of that will inform us in terms of what is unique about our perception and how we engage with the world. And so in some sense, they're complementary, right? Because that can give you a lot of detail. And so if we can think from that perspective, then together we can co-create new value. If I can just add one, and I want to intentionally take a more sort of a forceful view.

54:13See if you are if you accept that we have to move to this experience centric world okay then co-creation is is not like a choice okay we could do co-creation but we could also do other mechanisms we are saying no. Like if I want to create a Sharath experience I cannot produce it beforehand and keep it ready with me when Sharath wants, I give it to you. It must be co-created. It must be co-created. So I'm saying the answer is, it has to be. In the experience-centric world, you have to co-create. And because it's life-centric, human is at the helm of it. And human co-creates with the AI to deliver their experience.

54:54And Krishnan, since you gave us some examples about different companies, whether it's L 'Oreal or others. Yes. The second part of the question where this person was asking, how does this co-intelligence revolution affect specifically healthcare and life sciences? Yes. Are there any examples that you've studied that... True. In fact, let me just offer a couple of different scenarios. I mean, there is one that we talk about as open evidence, which I think in the US is at least 30, 40 % of the doctors have started using that. as a, in a way, it's a more fine-tuned, you know, chatbot, but trained on medical journals and so on.

55:40So more authentic information, impossible for humans to keep pace with the amount of knowledge that exists, but using the open evidence, the doctors can take a more informed decision about treatment protocols and so on. That's one example. At the other end of the spectrum, in terms of medical research and so on, I mean, there's a fascinating example that we start off with at IIT Madras with the Sudha Gopalakrishnan Brain Center. They have now

56:13created, they've just released something called Dharani, which is a database of human fetal brain. Okay, so this is the world's largest collection of human fetal brain. So that means they are actually mapping fetal brains, the whole brains, they have released it. Now, but this is all petabyte kind of information. So they are now forcing organizations like NVIDIA to come in and say, how can we now use AI in a much more effective way to deal with such kind of information? I produced this data, but how will now doctors use this to make certain decisions? So that's the frontier in terms of saying how it's forcing new kinds of, you know, AI capabilities to be built to process this.

57:02Now, it's definitely fascinating. And while this is not my area of expertise, I definitely see people at Google working on areas like MedGem, people who've tried to map the connectome. So a lot of what you're describing. But also in terms of healthcare, I think this is on the medical research side, I just want to also add that there's this whole area and we give different examples where you have lots of, for example, helping nurse practitioners engage better with patients like who might have dementia or people who have mental health issues. Those are very, very complex. But with these things, the people can speak in the natural language.

57:36So you can better understand what they're going through, right? Or what anxieties they have, what symptoms they experience in terms of pain or mental well-being and so on. because those are all very soft issues and they may express themselves through words, images, and so on. So I think the practitioners who are taking care of them can better connect with their lived experience. I think that's where also the power lies so that then you can adjust the way in which a certain treatment plan works because before that was missing. It was very hard for them to kind of share that, right, in terms of actually affecting the protocol.

58:15in terms of the delivery of the healthcare. I think we also have some questions from Googlers here. So, I'll request someone to hand a mic there. So, hi, I'm Yasmin. And my question is based on managing risks in co-intelligent systems. So, what's one thing that you believe is often overlooked, like a subtle risk that's often overlooked in organizations adopting AI? So, you're saying that, so I understood your question, right? Which is the risk that is often overlooked? Okay, so that's a very interesting question, actually. So if I were to think about all of the risks, I think one of the biggest risks that is overlooked is actually the risk of not following through on people's engagements with it.

59:05because a lot of the time we dismiss when someone, let's say, fails to utilize something in a certain way, right? It's almost like we have invert that and see it in a positive way. It's like seeing the glasses half full, right? And I think it's actually very important here because when people engage in the system, for example, most people in the use of child GPT, they use it in a one-shot way. They think the answer is good. Oh, it's not. But what they fail to see is that they need to further engage with it, right? Have that dialogue with it. But maybe they don't know that. And, of course, now we give some prompts, right?

59:46And so on. So actually, just understanding how people can better engage, and if they don't engage, why they don't engage, right? Or maybe it's not evident to them how to engage with AI. So in other words, it's almost like I'm flipping your question on its head and saying, what are the risks they're seeing? right? Or what are they fearful of because of which they don't engage. So it's almost like that I'm calling as a risk that is overlooked because if you understand that, then you can make it easier for them to engage with. You know, it's a little bit more subtle notion of thinking about risk.

1:00:22Yeah, good question. Hi, this is Shanmukhi. The idea of this co-intelligence is very fascinating that like which can answer the question does do ai replace humans i felt that but in present world especially students are very much habituated to relying on ai like using chat gpt to answer everything so what do you think like how should students approach this uh foundational getting foundational knowledge and critical thinking when ai can answer everything that they want. Yeah, so that's a great question. Being an educator, let me tackle that because I teach a course on innovation. So I've been now experimenting with the use of child GPT.

1:01:05You posed a question in terms of the students. I think the first implication is for us as educators, right, first, because so yes, from students' perspective, I think we talked about the fact that you don't outsource your thinking, but that may happen, right? So in a classroom setting, if I'm the educator, the question is that how do I ensure that you're not just outsourcing your thinking, getting answers? I think that is also part of your question. So one of the experiments that I've done is basically saying if you use child GPT, and I briefly talked about it, I really want to ensure that you're able to have that interactive conversation, right?

1:01:43And build your skills in doing that. But that's not something that I can just teach put up like a slide right it's something it comes through practice so in some sense uh i have to spend a lot of time which is what i found changing the nature of my assignment so in the old model i would have some assignments i'd give you something and some questions but then you may you can kind of paste it into chat gpt and you get some answers right and then you may work on that but is that really what i now should be doing this world probably not right so therefore i have to change like what the nature of the assignment is if it is to improve your thinking then it should be like how well I give you a situation, how well are you defining a problem, then you may use ChatGPT, maybe somebody else in your team uses it, well then talk with each other, hey, what did you get as outputs, right?

1:02:26What did you get? Oh, how did you prompt it with? How did you approach it? So it's almost like there is a conversation you had. So you may share your, you know, chat with the other person, the other person may share the chat back, which by the way is what I asked them to do. And then I asked them to then share it and say, hey, you know, is there something that you learned from how you interacted, right? So that they build those, oh, I didn't know. Oh, I didn't know I could ask it that way. I didn't know if I did this way, it would come back in this way. Because sometimes when it comes back with something that you think is incorrect, you might just say, hey, you can do better.

1:02:58This is not correct. And you'll say, oh, and here's why. So that interactive back and forth is very important. But it's very important to design the assignments in that way so that you can enhance that capacity of people. If I can add, I mean, I do a lot of research in this space in AI and education at the Center for Responsibility at IED Madras. So I'll answer this in two perspectives. One, what I recommend to students. And of course, this will vary depending on the age of the student and so on. I mean, there's a certain sense of maturity with age that you hope they develop and they understand the risk.

1:03:38I think you should keep using it on a daily basis. to get that habit. And as you use more, you learn how to use it and engage with the system better. I do know that kids love to take the shortcut. There's this beautiful answer right in front of you. Question is already asked. There's a tendency to just copy and paste it, which they will do. So it's the responsibility of the teacher to figure out what else, right? So not just the answer, but maybe we should ask them the question. but that's not on the student. The student wants to make their life easy. My submission to them is play around with it, build habit, and as you do.

1:04:22I mean, the other very important thing is not to trust it. Despite a lot of efforts by technology companies to get it right, right? I mean, the agentic systems, et cetera, there is, but I would start with a sense of don't trust everything that the thing says. You have to go and verify for yourself and build it. That's on the student. On the organization, that is the school or the educational institution, I think they have a very clear responsibility to develop more thoughtful systems, which means you can't have answers. Like you ask a question, it's spitting out answers. You can't design systems like that.

1:05:09You need to build guardrails in terms of reducing the amount of hallucinations that it does. So how to maximize those systems, that's another. So I think there are lots of responsibility on educational institutions to design the systems more thoughtfully. Yeah. And just to add, even in terms of, for example, what courses you should take. So I think sometimes we conflate the fact that, you know, this Chachipi is allowing us to talk in a natural language. That's one part of it. But the product itself, right, the content. So imagine if you're at the university, we provide courses. Students are still clueless about, like, you know, what courses you should take because we haven't designed that interface in a way in which it tries to understand, you know, what your career path might be, right?

1:05:56Based on that, saying which courses might be more suitable for you, what you have taken, what your goals are, and craft a very unique, personalized learning path for you. And then based on your experience of one course, what you should take. So I think right now, it's very course-centric. You have dropped-home box, different types of courses. But right there, you've lost me because that's not how I want to engage with it. I wanted, going back to everything we've been discussing, like the vegetable vendor, you know, here's kind of what my aspirations are, what I want to do, like in that finance example, right?

1:06:28And then saying, use that intelligence to go through all of the stuff there and say, hey, these are the courses that, you know, you might want to consider. That's very different, right? In that system helping you. And then, of course, within that, what you're saying is, then how does the learning take place? But I think there's this whole other set of things in terms of what does it mean to have, like you know a set of offerings that connect with you as a student in terms of your uh career path so we jokingly in the book say like we have coursera but you know maybe we should move to learn era or maybe growth era right where it's about the the growth of the individual and and and the the these ai systems are intelligent enough to take your inputs and to be able to craft that learning path for you.

1:07:13And I think there are lots of startups and various entities having this kind of frame of reference in terms of designing these systems features. So when they get designed, I think inside of it, the way learning will occur will also be different. Thank you. This is fascinating. And I think we could go on for a long time on this topic, but I know we're at time. I did want to say that my experience of reading the book, talking to you, it definitely feels like, yeah, this is not a point of evolution. I think that you're onto something when you call this the fourth industrial revolution. In the book, and you say there's a massive, massive change that we should be better prepared.

1:07:53The co-intelligence revolution. Yeah, the co-intelligence revolution, actually. And I think the other thing that also stood out was when you answered the question about what is the subtle risk that people overlook? It almost sounded like you're saying, look, more than the risk, there's the opportunity cost. The risk is that we overlook the fact there's a huge opportunity cost to not being involved in this at this stage. I love the fact that as you brought out some of the examples, it's clear that if we follow this path, we actually end up creating a much more inclusive world. Because it is not just that, you know, as the product manager working with a set of engineers and designers and various other professionals, I create a thing that tries as hard as possible to meet your need.

1:08:38but still it doesn't do enough unless you can get involved in it and say, hey, this is what I want in the moment and the entire product can adapt to it. And that reality is in our reach. There may still be a few steps that we have to get to to get there, but it looks like that's the reality we're working towards. And that's almost very, very positive because if you think of it, more inclusion is what we're shooting for as we're creating technology, putting it in the hands of more people, making it cheaper and so on. And underlying all of this, it sounds like you're also making the case that this is not just about, hey, go build a product based on these principles or go change this one thing.

1:09:14Could be education, could be health, could be something else. You're saying there's an entire ecosystem to build here that can have all the components working with each other in an intelligent way. And for me, the last thing that stands out is a little bit of that adage of, you know, the more things change, the more they remain the same. I think we've always told people that the higher-order skill that we want people to develop is critical thinking. We've always said that as long as you have empathy for another human, a lot of the other skills of designing things will follow through. Those are things that can be learned.

1:09:51They'll change. But it sounds like the importance of critical thinking and empathy for our fellow human, in many ways, are things that will still remain the same. And that's a big part of what will help people become part of this co-intelligence revolution in a meaningful way. Absolutely. Thank you. Thank you. Thank you, Venkat. Thank you, Krishnan. This was an absolutely fascinating talk. It's a fantastic way that you've summarized the key learnings. Fantastic. Let's take notes. Thank you very much.

1:10:35Thank you.